Scheduling highly parallel applications

ABSTRACT

Systems and methods for scheduling highly-parallel applications executed by high-performance computing systems. An example processing system may comprise: a control register and a processing core, communicatively coupled to the control register. The processing core may be configured to receive a node allocation request specifying an expected running time of an application and a requested number of nodes of a cluster of nodes; determine, in view of the node allocation request and a current load on the plurality of nodes, an actual number of nodes to be allocated to the application, wherein the actual number of nodes to be allocated to the application optimizes a cluster load criterion; and notify, using the control register, the application of the actual number of nodes.

The present disclosure generally relates to high-performance computer(HPC) systems and, more specifically, to instruction set architecturesupport for scheduling highly parallel applications.

BACKGROUND

A high-performance computing (HPC) system may include multiple computenodes grouped into one or more compute clusters. The compute nodes maybe managed by a control node.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of examples, and not by wayof limitation, and may be more fully understood with references to thefollowing detailed description when considered in connection with thefigures.

FIG. 1 schematically illustrates a block diagram of an examplehigh-performance computing system operating in accordance with one ormore aspects of the present disclosure.

FIGS. 2A-2B schematically illustrate various cluster configurations, inaccordance with one or more aspects of the present disclosure.

FIG. 3 is a block diagram of an example processing system operating inaccordance with one or more aspects of the present disclosure.

FIG. 4 depicts a flowchart of an example method 400 for determining theapplication configuration in a cluster of nodes, in accordance with oneor more aspects of the present disclosure.

FIG. 5 illustrates packed data types, in accordance with one or moreaspects of the present disclosure.

FIG. 6A illustrates elements of a processor micro-architecture, inaccordance with one or more aspects of the present disclosure.

FIG. 6B illustrates elements of a processor micro-architecture, inaccordance with one or more aspects of the present disclosure.

FIG. 7 is a block diagram of a processor operating in accordance withone or more aspects of the present disclosure.

FIG. 8 is a block diagram of a processor operating in accordance withone or more aspects of the present disclosure.

FIG. 9 is a block diagram of a system-on-a-chip operating in accordancewith one or more aspects of the present disclosure.

FIG. 10 is a block diagram of a computer system operating in accordancewith one or more aspects of the present disclosure.

FIG. 11 illustrates another example block diagram for a computing systemoperating in accordance with one or more aspects of the presentdisclosure.

FIG. 12 illustrates a diagrammatic representation of a machine in theexample form of a computing system operating in accordance with one ormore aspects of the present disclosure.

DETAILED DESCRIPTION

Described herein are systems and methods for scheduling highly parallelapplications.

A high-performance computing (HPC) system may include multiple computenodes grouped into one or more compute clusters. A batch schedulerresiding on a dedicated node or on one of the compute nodes may beemployed to schedule computing jobs (also referred to as applications)for execution on the compute nodes. One or more compute nodes may berequested for executing a given application, and an expected run timemay be specified. The scheduler may utilize these and other executionparameters to assign the applications to compute nodes for execution.

The scheduler may employ scheduling strategies that maximize the usagelevel of the compute nodes, in order to increase the overall system costefficiency. However, in common implementations, the average usage ofcompute nodes rarely exceeds 80% of their capacity, since the number ofnodes that is available for scheduling at a given moment in time may notnecessary be equal to a sum of requested number of nodes of one or moreapplications to be scheduled. In other words, the difference between theavailable number of nodes and the number of nodes allocated to one ormore applications may be less than the number of nodes that would berequired to schedule another application that is waiting for execution.

Under-utilization of compute nodes adversely affects the overall systemcost-efficiency, since even an inactive compute node consumes electricenergy for its operation and cooling. Utilizing at 80% capacity acluster of compute nodes that consumes between 20 and 40 megawatt (MW)of electric energy means that 4-8 MW of electric energy would be lostevery hour.

The present disclosure addresses the above-noted and other deficienciesby providing instruction set architecture (ISA) support for a batchscheduling method that causes the applications to increase or decreasethe application footprint (i.e., the number of compute nodes)dynamically at runtime. In accordance with one or more aspects of thepresent disclosure, a processor executing the batch scheduler mayimplement a control register for storing an address of a memory datastructure reflecting the current application configuration (e.g.,identifiers of the nodes that are assigned to the application). Theprocessor may further implement several instructions to manipulate thecontrol register. Such instructions may include Load Configuration(LDCONFIG) for reading the configuration, Store Configuration (STCONFIG)for storing the configuration, and Request Configuration Ownership(REQCONFIG) for requesting a lock of the application configuration datastructure.

A batch scheduler operating in accordance one or more aspects of thepresent disclosure may be programmed to determine cluster configurationsthat optimize a certain cluster load criterion. In an illustrativeexample, the batch scheduler may receive a node allocation request froman application that is waiting to be executed or is being executed bythe high-performance computing system. The node allocation request mayspecify the expected application and the requested number of nodes forexecuting the application.

Responsive to receiving the node allocation request, the batch schedulermay determine the cluster configuration that optimizes a chosen clusterload criterion. The batch scheduler may then utilize the processorcontrol register to notify the application of the actual number of nodesthat have been assigned to the application, as described in more detailsherein below.

Various aspects of the above referenced methods and systems aredescribed in details herein below by way of examples, rather than by wayof limitation.

Referring now to FIG. 1, example high-performance computing system 1000may include a master node 110 and a plurality of compute nodes 120A-120Ninterconnected by a network 135. Master node 110 may execute a batchscheduler 140, which may communicate to each of the nodes 120A-120N,e.g., via Message Passing Interface (MPI) messages 150.

Master node 110 may implement an input job queue 160 for queuingapplications 170A-170K waiting to be scheduled. Each application may bedescribed by a respective application identifier, the number of computenode requested for executing the application, and the expected runtimeof the application.

Batch scheduler 140 may be programmed to determine clusterconfigurations that optimize a certain cluster load criterion. Asschematically illustrated by FIG. 2A, batch scheduler 140 may generate aplurality of combinations of applications 170A-170K, and select acombination that minimizes the number of nodes that remain unassigned toany application at any given time. Alternatively, batch scheduler 140may select a combination that minimizes the total processing capacitythat remains unused within a given period of time. FIG. 2 schematicallyillustrates an example of scheduling a plurality of applications170A-170K to be executed by a plurality of compute nodes 120A-120Nwithin a certain time period. As shown in FIG. 2A, conventionalscheduling algorithms may produce schedules that have significant levelsof unused processing capacities (denoted as blocks 180).

Since some applications (e.g., client-server applications) may have anarchitecture that is easily adaptable to the dynamically changing numberof nodes, the batch scheduler 140 may improve the cluster efficiency byemploying scheduling methods that causes the applications to increase ordecrease the application footprint (i.e., the number of compute nodes)dynamically at runtime. Such scheduling methods may produce moreefficient, as compared with conventional methods, schedules that furtherminimize the levels of unused processing capacities, as schematicallyillustrated by FIG. 2B.

FIG. 3 schematically illustrates a block diagram of an exampleprocessing system operating in accordance with one or more aspects ofthe present disclosure. As schematically illustrated by FIG. 3,processing system 100 may include one or more processors 102 providingISA support for a batch scheduling method that instructs theapplications to increase or decrease the application footprintdynamically at runtime, in accordance with one or more aspects of thepresent disclosure.

In the illustrative example of FIG. 3, processor 102 includes one ormore execution units 408 configured to perform instructions of a certaininstruction set. Processor 102 is coupled to a processor bus 410 thattransmits data signals between processor 102 and other components ofsystem 100. Other elements of system 100 (e.g. graphics video card 412,memory controller hub 416, memory 420, I/O controller hub 430, wirelesstransceiver 426, flash BIOS 428, network controller 434, audiocontroller 436, serial expansion port 438, legacy I/O controller 440,etc.) may perform their conventional functions that are well known tothose familiar with the art.

Each processor 102 may comprise a plurality of registers, includinggeneral purpose registers and specialized registers. In certainimplementations, processing system 100 may also include various othercomponents not shown in FIG. 3. More detailed description of variouscomponents of processing system 100 is presented herein below.

Processor 102 may comprise a control register 122 that may be utilizedfor storing an address of a memory data structure reflecting the currentapplication configuration. In an illustrative example, the applicationconfiguration data structure may store an application identifier and thenumber of nodes to be assigned for executing the application. In anillustrative example, the application configuration data structure mayinitially be created by the application, which may record the requestednumber of nodes. The batch scheduler may eventually modify the number ofnodes assigned to the application, in an attempt to optimize a clusterload criterion.

Alternatively, the application configuration data structure may becreated by the batch scheduler, which may record the number of nodesassigned to the application. The creator of the applicationconfiguration data structure may store the address of the data structurein control register 122 using STCONFIG instruction. The application mayread the content of control register 122 using LDCONFIG instruction, andmay retrieve the address of the data structure storing the currentconfiguration of nodes for the application.

In certain implementations, processor 102 may further implement RequestConfiguration Ownership (REQCONFIG) for requesting a lock of theapplication configuration data structure before modifying the datastructure. In an illustrative example, the lock may be released uponexpiration of a pre-defined timeout since obtaining the lock.Alternatively, the lock may be released explicitly, by executingREQCONFIG command with a parameter indicating the lock release.

In certain implementations, the batch scheduler may determine that anapplication failed to release, within a pre-determined period of time, apreviously obtained lock of the application configuration datastructure. The batch scheduler may deem the application to benon-responsive and may initiate an appropriate recovery procedure (e.g.,by terminating the non-responsive application).

As noted herein above, the batch scheduler operating in accordance oneor more aspects of the present disclosure may be programmed to determinecluster configurations that optimize a certain cluster load criterion.In an illustrative example, the batch scheduler may receive a nodeallocation request from an application that is waiting to be executed oris being executed by the high-performance computing system. The nodeallocation request may specify the expected application and therequested number of nodes for executing the application. In certainimplementations, the node allocation request may further specify theminimum number of nodes required, a waiting preference parameterindicating whether an additional waiting time is preferred overallocation of a lesser number of nodes than requested, applicationpriority, and/or various other application parameters.

Responsive to receiving the node allocation request, the batch schedulermay determine the actual number of nodes to be allocated to theapplication. As noted herein above, the actual number of nodes may bechosen to optimize a certain cluster load criterion. In an illustrativeexample, the cluster load criterion may be determined as follows:L=Σ(n _(i) *t _(i))i=1, . . . n

wherein L denotes the cluster unused load, to be minimized by the batchscheduler, and n_(i) is the number of nodes that remain unused for aperiod of time t_(i).

In an illustrative example, the batch scheduler may generate a pluralityof combinations of applications of the plurality of applications to beexecuted by the cluster, determine the value of the cluster loadcriterion for each generated combination, and select the combinationthat is associated with the optimal (maximum or minimum) value of thecluster load criterion.

In various illustrative examples, the batch scheduler may determine acluster configuration that satisfies additional constraints derived fromvarious parameters of the node allocation request, such as the minimumnumber of nodes to be allocated to the application and/or the a waitingpreference parameter indicating whether an additional waiting time ispreferred over allocation of a lesser number of nodes than requested.

Responsive to determining the cluster configuration that optimizes thecluster load criterion, the batch scheduler may notify the applicationof the actual number of nodes that have been assigned to theapplication, as described in more details herein above.

FIG. 4 depicts a flowchart of an example method 400 for determining theapplication configuration in a cluster of nodes, in accordance with oneor more aspects of the present disclosure. Method 400 may be performedby processing logic that may comprise hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (such asoperations being performed by a functional unit), firmware or acombination thereof. In certain implementations, method 400 is performedby a batch scheduler being executed by example processing systems 100 ofFIG. 4.

Referring to FIG. 4, method 400 begins at block 410, where the processorimplementing the method may receive a node allocation request. The nodeallocation request may specify the expected application and therequested number of nodes for executing the application. In certainimplementations, the node allocation request may further specify variousadditional application execution parameters, as described in moredetails herein above.

At block 420, the processor may determine, in view of the nodeallocation request and a current load on the plurality of nodes, theapplication configuration for the requesting application, including thenumber of nodes to be allocated to the application. The applicationconfiguration may be chosen to optimize a certain cluster loadcriterion, as described in mode details herein above.

At block 430, the processor may store the application configuration,including the number of nodes, in the application configuration datastructure, as described in mode details herein above.

At block 440, the processor may store the address of the applicationconfiguration data structure in the control register, as described inmode details herein above.

At block 450, the processor may cause the application to be executedaccording to the determined application configuration.

Implementations of the present disclosure are not limited to computersystems. Alternative implementations of the present disclosure may beused in other devices such as handheld devices and embeddedapplications. Some examples of handheld devices include cellular phones,Internet Protocol devices, digital cameras, personal digital assistants(PDAs), and handheld PCs. Embedded applications may include a microcontroller, a digital signal processor (DSP), system on a chip, networkcomputers (NetPC), set-top boxes, network hubs, wide area network (WAN)switches, or any other system that may perform one or more instructionsin accordance with at least one implementation.

Referring again to FIG. 3, processor 102 includes one or more executionunits 408 to implement an algorithm that is to perform at least oneinstruction. One implementation may be described in the context of asingle processor desktop or server system, but alternativeimplementations may be included in a multiprocessor system. System 100is an example of the ‘hub’ system architecture. Processing system 100includes a processor 102 to process data signals. Processor 102, as oneillustrative example, includes a complex instruction set computer (CISC)microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, for example.Processor 102 is coupled to a processor bus 410 that transmits datasignals between the processor 102 and other components in the system100. The elements of system 100 (e.g. graphics accelerator 412, memorycontroller hub 416, memory 420, I/O controller hub 430, wirelesstransceiver 426, flash BIOS 428, network controller 434, audiocontroller 436, serial expansion port 438, I/O controller 430, etc.)perform their conventional functions that are well known to thosefamiliar with the art.

In one implementation, the processor 102 includes a Level 1 (L1)internal cache 404. Depending on the architecture, the processor 102 mayhave a single internal cache or multiple levels of internal caches.Other implementations include a combination of both internal andexternal caches depending on the particular implementation and needs.One or more of these caches may be set-associative and may allow addresslocking. As such, it may include an implementation whereby the cachecontroller implements the principles of locking and unlocking cacheways, as described herein. Register file 406 is to store different typesof data in various registers including integer registers, floating pointregisters, vector registers, banked registers, shadow registers,checkpoint registers, status registers, and instruction pointerregister.

Execution unit 408, including logic to perform integer and floatingpoint operations, also resides in the processor 102. The processor 102,in one implementation, includes a microcode (ucode) ROM to storemicrocode, which when executed, is to perform algorithms for certainmacroinstructions or handle complex scenarios. Here, microcode ispotentially updateable to handle logic bugs/fixes for processor 102. Forone implementation, execution unit 408 includes logic to handle a packedinstruction set 409. By including the packed instruction set 409 in theinstruction set of a general-purpose processor 102, along withassociated circuitry to execute the instructions, the operations used bymany multimedia applications may be performed using packed data in ageneral-purpose processor 102. Thus, many multimedia applications areaccelerated and executed more efficiently by using the full width of aprocessor's data bus for performing operations on packed data. Thispotentially eliminates the need to transfer smaller units of data acrossthe processor's data bus to perform one or more operations, one dataelement at a time.

Alternate implementations of an execution unit 408 may also be used inmicro controllers, embedded processors, graphics devices, DSPs, andother types of logic circuits. System 100 includes a memory 420. Memory420 includes a dynamic random access memory (DRAM) device, a staticrandom access memory (SRAM) device, flash memory device, or other memorydevice. Memory 420 stores instructions and/or data represented by datasignals that are to be executed by the processor 102.

Memory controller hub (MCH) 416 is coupled to the processor bus 410 andmemory 420. The processor 102 may communicate to the MCH 416 via aprocessor bus 410. The MCH 416 provides a high bandwidth memory path 418to memory 420 for instruction and data storage and for storage ofgraphics commands, data and textures. The MCH 416 is to direct datasignals between the processor 102, memory 420, and other components inthe system 100 and to bridge the data signals between processor bus 410,memory 420, and system I/O 422. In some implementations, the systemlogic chip 416 may provide a graphics port for coupling to a graphicscontroller 412. The MCH 416 is coupled to memory 420 through a memoryinterface 418. The graphics card 412 is coupled to the MCH 416 throughan Accelerated Graphics Port (AGP) interconnect 414. A cache may also beimplemented in the Memory Controller Hub 416 to provide a fasterresponse than memory 420. This cache may be set-associative and mayallow the locking of addresses, as described herein.

System 100 may use a proprietary hub interface bus 422 to couple the MCH416 to the I/O controller hub (ICH) 430. The ICH 430 provides directconnections to some I/O devices via a local I/O bus. The local I/O busis a high-speed I/O bus for connecting peripherals to the memory 420,chipset, and processor 102. Some examples are the audio controller,firmware hub (flash BIOS) 428, wireless transceiver 426, data storage424, legacy I/O controller containing user input and keyboardinterfaces, a serial expansion port such as Universal Serial Bus (USB),and a network controller 434. The data storage device 424 may comprise ahard disk drive, a floppy disk drive, a CD-ROM device, a flash memorydevice, or other mass storage device.

FIG. 5 illustrates various packed data type representations in processorregisters according to one implementation of the present disclosure.FIG. 5 illustrates data types for a packed byte 510, a packed word 520,and a packed doubleword (dword) 530 for 128 bits wide operands. Thepacked byte format 510 of this example is 128 bits long and containssixteen packed byte data elements. A byte is defined here as 8 bits ofdata. Information for each byte data element is stored in bit 7 throughbit 0 for byte 0, bit 15 through bit 8 for byte 1, bit 23 through bit 16for byte 2, and finally bit 120 through bit 127 for byte 15. Thus, allavailable bits are used in the register. This storage arrangementincreases the storage efficiency of the processor. As well, with sixteendata elements accessed, one operation may now be performed on sixteendata elements in parallel.

Generally, a data element is an individual piece of data that is storedin a single register or memory location with other data elements of thesame length. In packed data sequences relating to SSEx technology, thenumber of data elements stored in a XMM register is 128 bits divided bythe length in bits of an individual data element. Similarly, in packeddata sequences relating to MMX and SSE technology, the number of dataelements stored in an MMX register is 64 bits divided by the length inbits of an individual data element. Although the data types illustratedin FIG. 5 are 128 bit long, implementations may also operate with 64 bitwide or other sized operands. The packed word format 520 of this exampleis 128 bits long and contains eight packed word data elements. Eachpacked word contains sixteen bits of information. The packed doublewordformat 530 of FIG. 5 is 128 bits long and contains four packeddoubleword data elements. Each packed doubleword data element containsthirty two bits of information. A packed quadword is 128 bits long andcontains two packed quad-word data elements.

FIG. 6A is a block diagram illustrating an in-order pipeline and aregister renaming stage, out-of-order issue/execution pipeline accordingto at least one implementation of the disclosure. FIG. 6B is a blockdiagram illustrating an in-order architecture core and a registerrenaming logic, out-of-order issue/execution logic to be included in aprocessor according to at least one implementation of the disclosure.The solid lined boxes in FIG. 6A illustrate the in-order pipeline, whilethe dashed lined boxes illustrates the register renaming, out-of-orderissue/execution pipeline. Similarly, the solid lined boxes in FIG. 6Billustrate the in-order architecture logic, while the dashed lined boxesillustrates the register renaming logic and out-of-order issue/executionlogic.

In FIG. 6A, a processor pipeline 600 includes a fetch stage 602, alength decode stage 604, a decode stage 606, an allocation stage 608, arenaming stage 610, a scheduling (also known as a dispatch or issue)stage 612, a register read/memory read stage 614, an execute stage 616,a write back/memory write stage 618, an exception handling stage 622,and a commit stage 624.

In FIG. 6B, arrows denote a coupling between two or more units and thedirection of the arrow indicates a direction of data flow between thoseunits. FIG. 6B shows processor core 690 including a front end unit 630coupled to an execution engine unit 650, and both are coupled to amemory unit 670.

The core 690 may be a reduced instruction set computing (RISC) core, acomplex instruction set computing (CISC) core, a very long instructionword (VLIW) core, or a hybrid or alternative core type. As yet anotheroption, the core 690 may be a special-purpose core, such as, forexample, a network or communication core, compression engine, graphicscore, or the like.

The front end unit 630 includes a branch prediction unit 632 coupled toan instruction cache unit 634, which is coupled to an instructiontranslation lookaside buffer (TLB) 636, which is coupled to aninstruction fetch unit 638, which is coupled to a decode unit 640. Thedecode unit or decoder may decode instructions, and generate as anoutput one or more micro-operations, micro-code entry points,microinstructions, other instructions, or other control signals, whichare decoded from, or which otherwise reflect, or are derived from, theoriginal instructions. The decoder may be implemented using variousdifferent mechanisms. Examples of suitable mechanisms include, but arenot limited to, look-up tables, hardware implementations, programmablelogic arrays (PLAs), microcode read only memories (ROMs), etc. Theinstruction cache unit 634 is further coupled to a level 2 (L2) cacheunit 676 in the memory unit 670. The decode unit 640 is coupled to arename/allocator unit 652 in the execution engine unit 650.

The execution engine unit 650 includes the rename/allocator unit 652coupled to a retirement unit 654 and a set of one or more schedulerunit(s) 656. The scheduler unit(s) 656 represents any number ofdifferent schedulers, including reservations stations, centralinstruction window, etc. The scheduler unit(s) 656 is coupled to thephysical register file(s) unit(s) 658. Each of the physical registerfile(s) units 658 represents one or more physical register files,different ones of which store one or more different data types, such asscalar integer, scalar floating point, packed integer, packed floatingpoint, vector integer, vector floating point, status (e.g., aninstruction pointer that is the address of the next instruction to beexecuted), etc. The physical register file(s) unit(s) 658 is overlappedby the retirement unit 654 to illustrate various ways in which registerrenaming and out-of-order execution may be implemented (e.g., using areorder buffer(s) and a retirement register file(s), using a futurefile(s), a history buffer(s), and a retirement register file(s); using aregister maps and a pool of registers; etc.). Generally, thearchitectural registers are visible from the outside of the processor orfrom a programmer's perspective. The registers are not limited to anyknown particular type of circuit. Various different types of registersare suitable as long as they are capable of storing and providing dataas described herein. Examples of suitable registers include, but are notlimited to, dedicated physical registers, dynamically allocated physicalregisters using register renaming, combinations of dedicated anddynamically allocated physical registers, etc. The retirement unit 654and the physical register file(s) unit(s) 658 are coupled to theexecution cluster(s) 660. The execution cluster(s) 660 includes a set ofone or more execution units 162 and a set of one or more memory accessunits 664. The execution units 662 may perform various operations (e.g.,shifts, addition, subtraction, multiplication) and on various types ofdata (e.g., scalar floating point, packed integer, packed floatingpoint, vector integer, vector floating point). While someimplementations may include a number of execution units dedicated tospecific functions or sets of functions, other implementations mayinclude one execution unit or multiple execution units that all performall functions. The scheduler unit(s) 656, physical register file(s)unit(s) 658, and execution cluster(s) 660 are shown as being possiblyplural because certain implementations create separate pipelines forcertain types of data/operations (e.g., a scalar integer pipeline, ascalar floating point/packed integer/packed floating point/vectorinteger/vector floating point pipeline, and/or a memory access pipelinethat each have their own scheduler unit, physical register file(s) unit,and/or execution cluster—and in the case of a separate memory accesspipeline, certain implementations are implemented in which the executioncluster of this pipeline has the memory access unit(s) 664). It shouldalso be understood that where separate pipelines are used, one or moreof these pipelines may be out-of-order issue/execution and the restin-order.

The set of memory access units 664 is coupled to the memory unit 670,which includes a data TLB unit 672 coupled to a data cache unit 674coupled to a level 2 (L2) cache unit 676. In one exemplaryimplementation, the memory access units 664 may include a load unit, astore address unit, and a store data unit, each of which is coupled tothe data TLB unit 672 in the memory unit 670. The L2 cache unit 676 iscoupled to one or more other levels of cache and eventually to a mainmemory. The L2 cache unit 676 may be set associative and may allow thelocking of addresses, as described herein.

By way of example, the register renaming, out-of-order issue/executioncore architecture may implement the pipeline 500 as follows: 1) theinstruction fetch 638 performs the fetch and length decoding stages 602and 604; 2) the decode unit 640 performs the decode stage 606; 3) therename/allocator unit 652 performs the allocation stage 608 and renamingstage 610; 4) the scheduler unit(s) 656 performs the schedule stage 612;5) the physical register file(s) unit(s) 658 and the memory unit 670perform the register read/memory read stage 614; the execution cluster660 perform the execute stage 616; 6) the memory unit 670 and thephysical register file(s) unit(s) 658 perform the write back/memorywrite stage 618; 7) various units may be involved in the exceptionhandling stage 622; and 8) the retirement unit 654 and the physicalregister file(s) unit(s) 658 perform the commit stage 624.

The core 690 may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with additional extensions such asNEON) of ARM Holdings of Sunnyvale, Calif.).

While register renaming is described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated implementation of theprocessor also includes a separate instruction and data cache units634/674 and a shared L2 cache unit 676, alternative implementations mayhave a single internal cache for both instructions and data, such as,for example, a Level 1 (L1) internal cache, or multiple levels ofinternal cache. In some implementations, the system may include acombination of an internal cache and an external cache that is externalto the core and/or the processor. Alternatively, all of the cache may beexternal to the core and/or the processor.

FIG. 7 is a block diagram of the micro-architecture for a processor 700that includes logic circuits to perform instructions in accordance withone implementation of the present disclosure. In some implementations,an instruction in accordance with one implementation may be implementedto operate on data elements having sizes of byte, word, doubleword,quadword, etc., as well as datatypes, such as single and doubleprecision integer and floating point datatypes. In one implementationthe in-order front end 701 is the part of the processor 700 that fetchesinstructions to be executed and prepares them to be used later in theprocessor pipeline. The front end 701 may include several units. In oneimplementation, the instruction prefetcher 726 fetches instructions frommemory and feeds them to an instruction decoder 728 which in turndecodes or interprets them. For example, in one implementation, thedecoder decodes a received instruction into one or more operationscalled “micro-instructions” or “micro-operations” (also called micro opor uops) that the machine may execute. In other implementations, thedecoder parses the instruction into an opcode and corresponding data andcontrol fields that are used by the micro-architecture to performoperations in accordance with one implementation. In one implementation,the trace cache 730 takes decoded uops and assembles them into programordered sequences or traces in the uop queue 734 for execution. When thetrace cache 730 encounters a complex instruction, the microcode ROM 732provides the uops needed to complete the operation.

Some instructions are converted into a single micro-op, whereas othersneed several micro-ops to complete the full operation. In oneimplementation, if more than four micro-ops are needed to complete aninstruction, the decoder 728 accesses the microcode ROM 732 to do theinstruction. For one implementation, an instruction may be decoded intoa small number of micro ops for processing at the instruction decoder728. In another implementation, an instruction may be stored within themicrocode ROM 732 should a number of micro-ops be needed to accomplishthe operation. The trace cache 730 refers to an entry point programmablelogic array (PLA) to determine a correct micro-instruction pointer forreading the micro-code sequences to complete one or more instructions inaccordance with one implementation from the micro-code ROM 732. Afterthe microcode ROM 732 finishes sequencing micro-ops for an instruction,the front end 701 of the machine resumes fetching micro-ops from thetrace cache 730.

The out-of-order execution engine 703 is where the instructions areprepared for execution. The out-of-order execution logic has a number ofbuffers to smooth out and re-order the flow of instructions to optimizeperformance as they go down the pipeline and get scheduled forexecution. The allocator logic allocates the machine buffers andresources that each uop needs in order to execute. The register renaminglogic renames logic registers onto entries in a register file. Theallocator also allocates an entry for each uop in one of the two uopqueues, one for memory operations and one for non-memory operations, infront of the instruction schedulers: memory scheduler, fast scheduler702, slow/general floating point scheduler 704, and simple floatingpoint scheduler 706. The uop schedulers 702, 704, 706 determine when auop is ready to execute based on the readiness of their dependent inputregister operand sources and the availability of the execution resourcesthe uops need to complete their operation. The fast scheduler 702 of oneimplementation may schedule on each half of the main clock cycle whilethe other schedulers may schedule once per main processor clock cycle.The schedulers arbitrate for the dispatch ports to schedule uops forexecution.

Register files 708, 710 sit between the schedulers 702, 704, 706, andthe execution units 712, 714, 716, 718, 720, 722, 724 in the executionblock 711. There is a separate register file 708, 710 for integer andfloating point operations, respectively. Each register file 708, 710, ofone implementation also includes a bypass network that may bypass orforward just completed results that have not yet been written into theregister file to new dependent uops. The integer register file 708 andthe floating point register file 710 are also capable of communicatingdata with the other. For one implementation, the integer register file708 is split into two separate register files, one register file for thelow order 32 bits of data and a second register file for the high order32 bits of data. The floating point register file 710 of oneimplementation has 128 bit wide entries because floating pointinstructions typically have operands from 64 to 128 bits in width.

The execution block 711 contains the execution units 712, 714, 716, 718,720, 722, 724, where the instructions are actually executed. Thissection includes the register files 708, 710, that store the integer andfloating point data operand values that the micro-instructions need toexecute. The processor 700 of one implementation is comprised of anumber of execution units: address generation unit (AGU) 712, AGU 714,fast ALU 716, fast ALU 718, slow ALU 720, floating point ALU 722,floating point move unit 724. For one implementation, the floating pointexecution blocks 722, 724, execute floating point, MMX, SIMD, and SSE,or other operations. The floating point ALU 722 of one implementationincludes a 64 bit by 64 bit floating point divider to execute divide,square root, and remainder micro-ops. For implementations of the presentdisclosure, instructions involving a floating point value may be handledwith the floating point hardware. In one implementation, the ALUoperations go to the high-speed ALU execution units 716, 718. The fastALUs 716, 718, of one implementation may execute fast operations with aneffective latency of half a clock cycle. For one implementation, mostcomplex integer operations go to the slow ALU 720 as the slow ALU 720includes integer execution hardware for long latency type of operations,such as a multiplier, shifts, flag logic, and branch processing. Memoryload/store operations are executed by the AGUs 712, 714. For oneimplementation, the integer ALUs 716, 718, 720 are described in thecontext of performing integer operations on 64 bit data operands. Inalternative implementations, the ALUs 716, 718, 720 may be implementedto support a variety of data bits including 16, 32, 128, 756, etc.Similarly, the floating point units 722, 724 may be implemented tosupport a range of operands having bits of various widths. For oneimplementation, the floating point units 722, 724 may operate on 128bits wide packed data operands in conjunction with SIMD and multimediainstructions.

In one implementation, the uops schedulers 702, 704, 706 dispatchdependent operations before the parent load has finished executing. Asuops are speculatively scheduled and executed in processor 700, theprocessor 700 also includes logic to handle memory misses. If a dataload misses in the data cache, there may be dependent operations inflight in the pipeline that have left the scheduler with temporarilyincorrect data. A replay mechanism tracks and re-executes instructionsthat use incorrect data. The dependent operations should be replayed andthe independent ones are allowed to complete. The schedulers and replaymechanism of one implementation of a processor are also designed tocatch instruction sequences for text string comparison operations.

The term “registers” may refer to the on-board processor storagelocations that are used as part of instructions to identify operands. Inother words, registers may be those that are usable from the outside ofthe processor (from a programmer's perspective). However, the registersof an implementation should not be limited in meaning to a particulartype of circuit. Rather, a register of an implementation is capable ofstoring and providing data, and performing the functions describedherein. The registers described herein may be implemented by circuitrywithin a processor using any number of different techniques, such asdedicated physical registers, dynamically allocated physical registersusing register renaming, combinations of dedicated and dynamicallyallocated physical registers, etc. In one implementation, integerregisters store thirty-two bit integer data. A register file of oneimplementation also contains eight multimedia SIMD registers for packeddata. For the discussions below, the registers are understood to be dataregisters designed to hold packed data, such as 64 bits wide MMXregisters (also referred to as ‘mm’ registers in some instances) inmicroprocessors enabled with the MMX™ technology from Intel Corporationof Santa Clara, Calif. These MMX registers, available in both integerand floating point forms, may operate with packed data elements thataccompany SIMD and SSE instructions. Similarly, 128 bits wide XMMregisters relating to SSE2, SSE3, SSE4, or beyond (referred togenerically as “SSEx”) technology may also be used to hold such packeddata operands. In one implementation, in storing packed data and integerdata, the registers do not need to differentiate between the two datatypes. In one implementation, integer and floating point are eithercontained in the same register file or different register files.Furthermore, in one implementation, floating point and integer data maybe stored in different registers or the same registers.

FIG. 8 is a block diagram of a single core processor and a multicoreprocessor 800 with integrated memory controller and graphics accordingto implementations of the disclosure. The solid lined boxes in FIG. 8illustrate a processor 800 with a single core 802A, a system agent 810,a set of one or more bus controller units 816, while the addition of thedashed lined boxes illustrates an alternative processor 800 withmultiple cores 802A-N, a set of one or more integrated memory controllerunit(s) 814 in the system agent unit 810, and an integrated graphicslogic 808.

The memory hierarchy includes one or more levels of cache within thecores, a set or one or more shared cache units 806, and external memory(not shown) coupled to the set of integrated memory controller units814. The set of shared cache units 806 may include one or more mid-levelcaches, such as level 2 (L2), level 3 (L3), level 4 (L4), or otherlevels of cache, a last level cache (LLC), and/or combinations thereof.This set of shared cache units 806 may be set associative and may allowthe locking of addresses, as described herein. While in oneimplementation a ring based interconnect unit 812 interconnects theintegrated graphics logic 808, the set of shared cache units 806, andthe system agent unit 810, alternative implementations may use anynumber of well-known techniques for interconnecting such units.

In some implementations, one or more of the cores 802A-N are capable ofmulti-threading.

The system agent 810 includes those components coordinating andoperating cores 802A-N. The system agent unit 810 may include forexample a power control unit (PCU) and a display unit. The PCU may be orinclude logic and components needed for regulating the power state ofthe cores 802A-N and the integrated graphics logic 808. The display unitis for driving one or more externally connected displays.

The cores 802A-N may be homogenous or heterogeneous in terms ofarchitecture and/or instruction set. For example, some of the cores802A-N may be in order while others are out-of-order. As anotherexample, two or more of the cores 802A-N may be capable of execution thesame instruction set, while others may be capable of executing a subsetof that instruction set or a different instruction set. As a furtherexample, the cores may be different architecture.

The processor may include one or more different general-purposeprocessors, such as a Core™ i3, i5, i7, 2 Duo and Quad, Xeon™, Itanium™,Atom™, XScale™ or StrongARM™ processor, which are available from IntelCorporation, of Santa Clara, Calif. For example, one core may be a Corei7™ core while another core of the processor may be an Atom™ core.Alternatively, the processor may be from another company, such as ARMHoldings, Ltd, MIPS, etc. The processor may be a special-purposeprocessor, such as, for example, a network or communication processor,compression engine, graphics processor, co-processor, embeddedprocessor, or the like. The processor may be implemented on one or morechips. The processor 800 may be a part of and/or may be implemented onone or more substrates using any of a number of process technologies,such as, for example, BiCMOS, CMOS, or NMOS.

Referring now to FIG. 9, shown is a block diagram of a SoC 900 inaccordance with an implementation of the present disclosure. Similarelements in FIG. 9 bear like reference numerals. Also, dashed linedboxes are features on more advanced SoCs. In FIG. 9, an interconnectunit(s) 902 is coupled to: an application processor 910 which includes aset of one or more cores 902A-N and shared cache unit(s) 906; a systemagent unit 910; a bus controller unit(s) 916; an integrated memorycontroller unit(s) 914; a set or one or more media processors 920 whichmay include integrated graphics logic 908, an image processor 924 forproviding still and/or video camera functionality, an audio processor926 for providing hardware audio acceleration, and a video processor 928for providing video encode/decode acceleration; an static random accessmemory (SRAM) unit 930; a direct memory access (DMA) unit 932; and adisplay unit 940 for coupling to one or more external displays.

Implementations may be implemented in many different system types.Referring now to FIG. 10, shown is a block diagram of a multiprocessorsystem 1000 in accordance with some implementations. As shown in FIG.10, multiprocessor system 1000 is a point-to-point interconnect system,and includes a first processor 1070 and a second processor 1080 coupledvia a point-to-point interconnect 1050. As shown in FIG. 10, each ofprocessors 1070 and 1080 may be multicore processors, including firstand second processor cores (i.e., processor cores 1074 a and 1074 b andprocessor cores 1084 a and 1084 b), although potentially many more coresmay be present in the processors. The processors each may include hybridwrite mode logics in accordance with an implementation of the present.

While shown with two processors 1070, 1080, it is to be understood thatthe scope of the present disclosure is not so limited. In otherimplementations, one or more additional processors may be present in agiven processor.

Processors 1070 and 1080 are shown including integrated memorycontroller units 8102 and 8102, respectively. Processor 1070 alsoincludes as part of its bus controller units point-to-point (P-P)interfaces 1076 and 1078; similarly, second processor 1080 includes P-Pinterfaces 1086 and 1088. Processors 1070, 1080 may exchange informationvia a point-to-point (P-P) interface 1050 using P-P interface circuits1078, 1088. As shown in FIG. 10, IMCs 1072 and 1082 couple theprocessors to respective memories, namely a memory 1032 and a memory1034, which may be portions of main memory locally attached to therespective processors.

Processors 1070, 1080 may each exchange information with a chipset 1090via individual P-P interfaces 1052, 1054 using point to point interfacecircuits 1076, 1094, 1086, 1098. Chipset 1090 may also exchangeinformation with a high-performance graphics circuit 1038 via ahigh-performance graphics interface 1039.

A shared cache (not shown) may be included in either processor oroutside of both processors, yet connected with the processors via P-Pinterconnect, such that either or both processors' local cacheinformation may be stored in the shared cache if a processor is placedinto a low power mode.

Chipset 1090 may be coupled to a first bus 1016 via an interface 1096.In one implementation, first bus 1016 may be a Peripheral ComponentInterconnect (PCI) bus, or a bus such as a PCI Express bus or anotherthird generation I/O interconnect bus, although the scope of the presentdisclosure is not so limited.

As shown in FIG. 10, various I/O devices 1014 may be coupled to firstbus 1016, along with a bus bridge 1018 which couples first bus 1016 to asecond bus 1020. In one implementation, second bus 1020 may be a low pincount (LPC) bus. Various devices may be coupled to second bus 1020including, for example, a keyboard and/or mouse 1022, communicationdevices 1027 and a storage unit 1028 such as a disk drive or other massstorage device which may include instructions/code and data 1030, in oneimplementation. Further, an audio I/O 1024 may be coupled to second bus1020. Note that other architectures are possible. For example, insteadof the point-to-point architecture of FIG. 10, a system may implement amulti-drop bus or other such architecture.

Turning next to FIG. 11, an implementation of a system on-chip (SOC)design in accordance with implementations of the disclosure is depicted.As an illustrative example, SOC 1100 is included in user equipment (UE).In one implementation, UE refers to any device to be used by an end-userto communicate, such as a hand-held phone, smartphone, tablet,ultra-thin notebook, notebook with broadband adapter, or any othersimilar communication device. A UE may connect to a base station ornode, which may correspond in nature to a mobile station (MS) in a GSMnetwork.

Here, SOC 1100 includes 2 cores—1106 and 1107. Similar to the discussionabove, cores 1106 and 1107 may conform to an Instruction SetArchitecture, such as a processor having the Intel® Architecture Core™,an Advanced Micro Devices, Inc. (AMD) processor, a MIPS-based processor,an ARM-based processor design, or a customer thereof, as well as theirlicensees or adopters. Cores 1106 and 1107 are coupled to cache control1108 that is associated with bus interface unit 1109 and L2 cache 1110to communicate with other parts of system 1100. Interconnect 1111includes an on-chip interconnect, such as an IOSF, AMBA, or otherinterconnects discussed above, which may implement one or more aspectsof the described disclosure.

Interconnect 1111 provides communication channels to the othercomponents, such as a Subscriber Identity Module (SIM) 1130 to interfacewith a SIM card, a boot rom 1135 to hold boot code for execution bycores 1106 and 1107 to initialize and boot SOC 1100, a SDRAM controller1140 to interface with external memory (e.g. DRAM 1160), a flashcontroller 1145 to interface with persistent or non-volatile memory(e.g. Flash 1165), a peripheral control 1150 (e.g. Serial PeripheralInterface) to interface with peripherals, video codecs 1120 and Videointerface 1125 to display and receive input (e.g. touch enabled input),GPU 1115 to perform graphics related computations, etc. Any of theseinterfaces may incorporate aspects of the implementations describedherein.

In addition, the system illustrates peripherals for communication, suchas a Bluetooth module 1170, modem 1175 (e.g., 3G, 4G, Long TermEvolution (LTE), LTE-Advanced, etc.), GPS 1180, Wi-Fi 1185, Zigbee (notshown), and Z-Wave (not shown). Note as stated above, a UE includes aradio for communication. As a result, these peripheral communicationmodules may not all be included. However, in a UE some form of a radiofor external communication should be included.

FIG. 12 illustrates a diagrammatic representation of a machine in theexample form of a computing system 1200 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeimplementations, the machine may be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client device in aclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a game console, a cellular telephone, a digitalcamera, a handheld PC, a web appliance, a server, a network router,switch or bridge, micro controller, a digital signal processor (DSP),system on a chip, network computers (NetPC), network hubs, wide areanetwork (WAN) switches, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine. Further, while only a single machine is illustrated forthe system architecture 100, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein. Implementations are not limited tocomputer systems.

The computing system 1200 includes a processing device 1202, main memory1204 (e.g., read-only memory (ROM), flash memory, dynamic random accessmemory (DRAM) (such as synchronous DRAM (SDRAM) or DRAM (RDRAM), etc.),a static memory 1206 (e.g., flash memory, static random access memory(SRAM), etc.), and a data storage device 1216, which communicate witheach other via a bus 1208.

Processing device 1202 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device may be complex instruction setcomputing (CISC) microprocessor, reduced instruction set computer (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 1202may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. In one implementation, processing device 1202 may includeone or processing cores. The processing device 1202 is configured toexecute the processing logic 1226 for performing the operationsdiscussed herein. In one implementation, processing device 1202 may bepart of the system architecture 100 of FIG. 1A or SoC 190 of FIG. 1B.Alternatively, the computing system 1200 may include other components asdescribed herein. It should be understood that the core may supportmultithreading (executing two or more parallel sets of operations orthreads), and may do so in a variety of ways including time slicedmultithreading, simultaneous multithreading (where a single physicalcore provides a logical core for each of the threads that physical coreis simultaneously multithreading), or a combination thereof (e.g., timesliced fetching and decoding and simultaneous multithreading thereaftersuch as in the Intel® Hyperthreading technology).

Computing system 1200 is representative of processing systems based onthe PENTIUM III™, PENTIUM 4™, Celeron™, Xeon™, Itanium, XScale™,StrongARM™, Core™, Core 2™, Atom™, and/or Intel® Architecture Core™,such as an i3, i5, i7 microprocessors available from Intel Corporationof Santa Clara, Calif., although other systems (including PCs havingother microprocessors, engineering workstations, set-top boxes and thelike) may also be used. However, understand that other low powerprocessors such as available from Advanced Micro Devices, Inc. (AMD) ofSunnyvale, Calif., a MIPS-based design from MIPS Technologies, Inc. ofSunnyvale, Calif., an ARM-based design licensed from ARM Holdings, Ltd.or customer thereof, or their licensees or adopters may instead bepresent in other implementations such as an Apple A5/A6 processor, aQualcomm Snapdragon processor, or TI OMAP processor. In oneimplementation, processing device 1202 executes a version of theWINDOWS™ operating system available from Microsoft Corporation ofRedmond, Wash., although other operating systems (OS X, UNIX, Linux,Android, iOS, Symbian, for example), embedded software, and/or graphicaluser interfaces, may also be used. Thus, implementations of the presentdisclosure are not limited to any specific combination of hardwarecircuitry and software. One implementation may be described in thecontext of a single processor desktop or server system, but alternativeimplementations may be included in a multiprocessor system. Computingsystem 1200 may be an example of a ‘hub’ system architecture.

The computing system 1200 may further include a network interface device1222 communicably coupled to a network 1218. The computing system 1200also may include a display device 1210 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 1212(e.g., a keyboard), a cursor control device 1214 (e.g., a mouse), asignal generation device 1220 (e.g., a speaker), or other peripheraldevices. Furthermore, computing system 1200 may include a graphicsprocessing unit (not illustrated), a video processing unit (notillustrated) and an audio processing unit (not illustrated). In anotherimplementation, the computing system 1200 may include a chipset (notillustrated), which refers to a group of integrated circuits, or chips,that are designed to work with the processing device 1202 and controlscommunications between the processing device 1202 and external devices.For example, the chipset may be a set of chips on a motherboard thatlinks the processing device 1202 to very high-speed devices, such asmain memory 1204 and graphic controllers, as well as linking theprocessing device 1202 to lower-speed peripheral buses of peripherals,such as USB, PCI or ISA buses.

The data storage device 1216 may include a computer-readable storagemedium 1224 on which is stored instructions 1226 embodying any one ormore of the methodologies of functions described herein. Theinstructions 1226 may also reside, completely or at least partially,within the main memory 1204 as instructions 1226 and/or within theprocessing device 1202 as processing logic 1226 during execution thereofby the computing system 1200; the main memory 1204 and the processingdevice 1202 also constituting computer-readable storage media.

The computer-readable storage medium 1224 may also be used to storeinstructions 1226 utilizing the processing device 1202, such asdescribed with respect to FIG. 1A, and/or a software library containingmethods that call the above applications. While the computer-readablestorage medium 1224 is shown in an example implementation to be a singlemedium, the term “computer-readable storage medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of instructions. The term “computer-readablestorage medium” shall also be taken to include any medium that iscapable of storing, encoding or carrying a set of instruction forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present implementations. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media.

While the present disclosure has been described with respect to alimited number of implementations, those skilled in the art willappreciate numerous modifications and variations therefrom. It isintended that the appended claims cover all such modifications andvariations as fall within the true spirit and scope of this presentdisclosure.

In the description herein, numerous specific details are set forth, suchas examples of specific types of processors and system configurations,specific hardware structures, specific architectural and microarchitectural details, specific register configurations, specificinstruction types, specific system components, specificmeasurements/heights, specific processor pipeline stages and operationetc. in order to provide a thorough understanding of the presentdisclosure. It will be apparent, however, to one skilled in the art thatthese specific details need not be employed to practice the presentdisclosure. In other instances, well known components or methods, suchas specific and alternative processor architectures, specific logiccircuits/code for described algorithms, specific firmware code, specificinterconnect operation, specific logic configurations, specificmanufacturing techniques and materials, specific compilerimplementations, specific expression of algorithms in code, specificpower down and gating techniques/logic and other specific operationaldetails of computer system have not been described in detail in order toavoid unnecessarily obscuring the present disclosure.

The implementations are described with reference to hybrid-threading inspecific integrated circuits, such as in computing platforms ormicroprocessors. The implementations may also be applicable to othertypes of integrated circuits and programmable logic devices. Forexample, the disclosed implementations are not limited to desktopcomputer systems or portable computers, such as the Intel® Ultrabooks™computers. And may be also used in other devices, such as handhelddevices, tablets, other thin notebooks, systems on a chip (SOC) devices,and embedded applications. Some examples of handheld devices includecellular phones, Internet protocol devices, digital cameras, personaldigital assistants (PDAs), and handheld PCs. Embedded applicationstypically include a microcontroller, a digital signal processor (DSP), asystem on a chip, network computers (NetPC), set-top boxes, networkhubs, wide area network (WAN) switches, or any other system that mayperform the functions and operations taught below. It is described thatthe system may be any kind of computer or embedded system. The disclosedimplementations may especially be used for low-end devices, likewearable devices (e.g., watches), electronic implants, sensory andcontrol infrastructure devices, controllers, supervisory control anddata acquisition (SCADA) systems, or the like. Moreover, theapparatuses, methods, and systems described herein are not limited tophysical computing devices, but may also relate to softwareoptimizations for energy conservation and efficiency. As will becomereadily apparent in the description below, the implementations ofmethods, apparatuses, and systems described herein (whether in referenceto hardware, firmware, software, or a combination thereof) are vital toa ‘green technology’ future balanced with performance considerations.

Although the implementations herein are described with reference to aprocessor, other implementations are applicable to other types ofintegrated circuits and logic devices. Similar techniques and teachingsof implementations of the present disclosure may be applied to othertypes of circuits or semiconductor devices that may benefit from higherpipeline throughput and improved performance. The teachings ofimplementations of the present disclosure are applicable to anyprocessor or machine that performs data manipulations. However, thepresent disclosure is not limited to processors or machines that perform512 bit, 256 bit, 128 bit, 64 bit, 32 bit, or 16 bit data operations andmay be applied to any processor and machine in which manipulation ormanagement of data is performed. In addition, the description hereinprovides examples, and the accompanying drawings show various examplesfor the purposes of illustration. However, these examples should not beconstrued in a limiting sense as they are merely intended to provideexamples of implementations of the present disclosure rather than toprovide an exhaustive list of all possible implementations ofimplementations of the present disclosure.

Although the below examples describe instruction handling anddistribution in the context of execution units and logic circuits, otherimplementations of the present disclosure may be accomplished by way ofa data or instructions stored on a machine-readable, tangible medium,which when performed by a machine cause the machine to perform functionsconsistent with at least one implementation of the disclosure. In oneimplementation, functions associated with implementations of the presentdisclosure are embodied in machine-executable instructions. Theinstructions may be used to cause a general-purpose or special-purposeprocessor that is programmed with the instructions to perform the stepsof the present disclosure. Implementations of the present disclosure maybe provided as a computer program product or software which may includea machine or computer-readable medium having stored thereon instructionswhich may be used to program a computer (or other electronic devices) toperform one or more operations according to implementations of thepresent disclosure. Alternatively, operations of implementations of thepresent disclosure might be performed by specific hardware componentsthat contain fixed-function logic for performing the operations, or byany combination of programmed computer components and fixed-functionhardware components.

Instructions used to program logic to perform implementations of thedisclosure may be stored within a memory in the system, such as DRAM,cache, flash memory, or other storage. Furthermore, the instructions maybe distributed via a network or by way of other computer readable media.Thus a machine-readable medium may include any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer), but is not limited to, floppy diskettes, optical disks,Compact Disc, Read-Only Memory (CD-ROMs), and magneto-optical disks,Read-Only Memory (ROMs), Random Access Memory (RAM), ErasableProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), magnetic or optical cards, flashmemory, or a tangible, machine-readable storage used in the transmissionof information over the Internet via electrical, optical, acoustical orother forms of propagated signals (e.g., carrier waves, infraredsignals, digital signals, etc.). Accordingly, the computer-readablemedium includes any type of tangible machine-readable medium suitablefor storing or transmitting electronic instructions or information in aform readable by a machine (e.g., a computer).

A design may go through various stages, from creation to simulation tofabrication. Data representing a design may represent the design in anumber of manners. First, as is useful in simulations, the hardware maybe represented using a hardware description language or anotherfunctional description language. Additionally, a circuit level modelwith logic and/or transistor gates may be produced at some stages of thedesign process. Furthermore, most designs, at some stage, reach a levelof data representing the physical placement of various devices in thehardware model. In the case where conventional semiconductor fabricationtechniques are used, the data representing the hardware model may be thedata specifying the presence or absence of various features on differentmask layers for masks used to produce the integrated circuit. In anyrepresentation of the design, the data may be stored in any form of amachine readable medium. A memory or a magnetic or optical storage suchas a disc may be the machine readable medium to store informationtransmitted via optical or electrical wave modulated or otherwisegenerated to transmit such information. When an electrical carrier waveindicating or carrying the code or design is transmitted, to the extentthat copying, buffering, or re-transmission of the electrical signal isperformed, a new copy is made. Thus, a communication provider or anetwork provider may store on a tangible, machine-readable medium, atleast temporarily, an article, such as information encoded into acarrier wave, embodying techniques of implementations of the presentdisclosure.

A module as used herein refers to any combination of hardware, software,and/or firmware. As an example, a module includes hardware, such as amicro-controller, associated with a non-transitory medium to store codeadapted to be executed by the micro-controller. Therefore, reference toa module, in one implementation, refers to the hardware, which isspecifically configured to recognize and/or execute the code to be heldon a non-transitory medium. Furthermore, in another implementation, useof a module refers to the non-transitory medium including the code,which is specifically adapted to be executed by the microcontroller toperform predetermined operations. And as may be inferred, in yet anotherimplementation, the term module (in this example) may refer to thecombination of the microcontroller and the non-transitory medium. Oftenmodule boundaries that are illustrated as separate commonly vary andpotentially overlap. For example, a first and a second module may sharehardware, software, firmware, or a combination thereof, whilepotentially retaining some independent hardware, software, or firmware.In one implementation, use of the term logic includes hardware, such astransistors, registers, or other hardware, such as programmable logicdevices.

Use of the phrase ‘configured to,’ in one implementation, refers toarranging, putting together, manufacturing, offering to sell, importingand/or designing an apparatus, hardware, logic, or element to perform adesignated or determined task. In this example, an apparatus or elementthereof that is not operating is still ‘configured to’ perform adesignated task if it is designed, coupled, and/or interconnected toperform said designated task. As a purely illustrative example, a logicgate may provide a 0 or a 1 during operation. But a logic gate‘configured to’ provide an enable signal to a clock does not includeevery potential logic gate that may provide a 1 or 0. Instead, the logicgate is one coupled in some manner that during operation the 1 or 0output is to enable the clock. Note once again that use of the term‘configured to’ does not require operation, but instead focus on thelatent state of an apparatus, hardware, and/or element, where in thelatent state the apparatus, hardware, and/or element is designed toperform a particular task when the apparatus, hardware, and/or elementis operating.

Furthermore, use of the phrases ‘to,’ ‘capable of/to,’ and or ‘operableto,’ in one implementation, refers to some apparatus, logic, hardware,and/or element designed in such a way to enable use of the apparatus,logic, hardware, and/or element in a specified manner. Note as abovethat use of to, capable to, or operable to, in one implementation,refers to the latent state of an apparatus, logic, hardware, and/orelement, where the apparatus, logic, hardware, and/or element is notoperating but is designed in such a manner to enable use of an apparatusin a specified manner.

A value, as used herein, includes any known representation of a number,a state, a logical state, or a binary logical state. Often, the use oflogic levels, logic values, or logical values is also referred to as 1'sand 0's, which simply represents binary logic states. For example, a 1refers to a high logic level and 0 refers to a low logic level. In oneimplementation, a storage cell, such as a transistor or flash cell, maybe capable of holding a single logical value or multiple logical values.However, other representations of values in computer systems have beenused. For example the decimal number ten may also be represented as abinary value of 1010 and a hexadecimal letter A. Hexadecimal values mayalso be represented with a prefix, such as “0x.” Therefore, a valueincludes any representation of information capable of being held in acomputer system.

Moreover, states may be represented by values or portions of values. Asan example, a first value, such as a logical one, may represent adefault or initial state, while a second value, such as a logical zero,may represent a non-default state. In addition, the terms reset and set,in one implementation, refer to a default and an updated value or state,respectively. For example, a default value potentially includes a highlogical value, i.e. reset, while an updated value potentially includes alow logical value, i.e. set. Note that any combination of values may beutilized to represent any number of states.

The implementations of methods, hardware, software, firmware or code setforth above may be implemented via instructions or code stored on amachine-accessible, machine readable, computer accessible, or computerreadable medium which are executable by a processing element. Anon-transitory machine-accessible/readable medium includes any mechanismthat provides (i.e., stores and/or transmits) information in a formreadable by a machine, such as a computer or electronic system. Forexample, a non-transitory machine-accessible medium includesrandom-access memory (RAM), such as static RAM (SRAM) or dynamic RAM(DRAM); ROM; magnetic or optical storage medium; flash memory devices;electrical storage devices; optical storage devices; acoustical storagedevices; other form of storage devices for holding information receivedfrom transitory (propagated) signals (e.g., carrier waves, infraredsignals, digital signals); etc., which are to be distinguished from thenon-transitory mediums that may receive information there from.

Reference throughout this specification to “one implementation” or “animplementation” means that a particular feature, structure, orcharacteristic described in connection with the implementation isincluded in at least one implementation of the present disclosure. Thus,the appearances of the phrases “in one implementation” or “in animplementation” in various places throughout this specification are notnecessarily all referring to the same implementation. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more implementations.

In the present specification, a detailed description has been given withreference to specific example implementations. It will, however, beevident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the disclosure asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense. Furthermore, the foregoing use of implementation andother exemplarily language does not necessarily refer to the sameimplementation or the same example, but may refer to different anddistinct implementations, as well as potentially the sameimplementation.

The following examples pertain to further implementations.

Example 1 is a processing system, comprising: a control register; and aprocessing core, communicatively coupled to the control register;wherein the processing core is configured to: receive a node allocationrequest specifying an expected running time of an application and arequested number of nodes of a cluster of nodes; determine, in view ofthe node allocation request and a current load on the plurality ofnodes, an actual number of nodes to be allocated to the application,wherein the actual number of nodes to be allocated to the applicationoptimizes a cluster load criterion; and notify, using the controlregister, the application of the actual number of nodes.

Example 2 is the processing system of Example 1, wherein notifying theapplication of the actual number of nodes comprises: storing the actualnumber of nodes in a memory data structure that is employed to store aconfiguration of nodes associated with the application; and storing anaddress of the memory data structure in the control register.

Example 3 is the processing system of Example 2, wherein storing theaddress of the memory data structure in the control register comprisesobtaining a lock of the control register.

Example 4 is the processing system of Example 1, wherein the processingcore is further configured to: cause the application to be executedusing the actual number of nodes.

Example 5 is the processing system of Example 1, wherein the clusterload criterion reflects a number of nodes that remain unassigned to anycurrently running application within a certain time period.

Example 6 is the processing system of Example 1, wherein the clusterload criterion reflects a number of nodes that remain unassigned to anyrunning application within a certain time period, multiplied by the timeperiod.

Example 7 is the processing system of Example 1, wherein determining theactual number of nodes further comprises: generating a plurality ofcombinations of applications to be scheduled; determining a value of thecluster load criterion for each generated combination; and selecting acombination that is associated with an optimal value of the cluster loadcriterion.

Example 8 is the processing system of Example 1, wherein the processingsystem is implemented as a System-on-Chip (SoC).

Example 9 is a method, comprising: receiving, by a processing device, anode allocation request specifying an expected running time of anapplication and a requested number of nodes of a cluster of nodes;determining, in view of the node allocation request and a current loadon the plurality of nodes, an actual number of nodes to be allocated tothe application, wherein the actual number of nodes to be allocated tothe application optimizes a cluster load criterion; and notifying, usinga control register of the processing system, the application of theactual number of nodes.

Example 10 is the method of Example 9, wherein notifying the applicationof the actual number of nodes comprises: storing the actual number ofnodes in a memory data structure that is employed to store aconfiguration of nodes associated with the application; and storing anaddress of the memory data structure in the control register.

Example 11 is the method of Example 10, wherein storing the address ofthe memory data structure in the control register comprises obtaining alock of the control register.

Example 12 is the method of Example 9, further comprising: causing theapplication to be executed using the actual number of nodes.

Example 13 is the method of Example 9, wherein the cluster loadcriterion reflects a number of nodes that remain unassigned to anycurrently running application within a certain time period.

Example 14 is the method of Example 9, wherein the cluster loadcriterion reflects a number of nodes that remain unassigned to anyrunning application within a certain time period, multiplied by the timeperiod.

Example 15 is the method of Example 9, wherein determining the actualnumber of nodes further comprises: generating a plurality ofcombinations of applications to be scheduled; determining a value of thecluster load criterion for each generated combination; and selecting acombination that is associated with an optimal value of the cluster loadcriterion.

Example 16 is an apparatus comprising: a memory; and a processing devicecoupled to the memory, the processing device to perform the method ofany of examples 9-15.

Example 17 is a non-transitory computer-readable storage mediumcomprising executable instructions that, when executed by a processingsystem, cause the processing system to perform operations comprising:receiving a node allocation request specifying an expected running timeof an application and a requested number of nodes of a cluster of nodes;determining, in view of the node allocation request and a current loadon the plurality of nodes, an actual number of nodes to be allocated tothe application, wherein the actual number of nodes to be allocated tothe application optimizes a cluster load criterion; and notifying, usinga control register of the processing system, the application of theactual number of nodes.

Example 18 is the non-transitory computer-readable storage medium ofExample 17, wherein notifying the application of the actual number ofnodes comprises: storing the actual number of nodes in a memory datastructure that is employed to store a configuration of nodes associatedwith the application; and storing an address of the memory datastructure in the control register.

Example 19 is the non-transitory computer-readable storage medium ofExample 18, wherein storing the address of the memory data structure inthe control register comprises obtaining a lock of the control register.

Example 20 is the non-transitory computer-readable storage medium ofExample 17, further comprising executable instructions causing theprocessing system to: cause the application to be executed using theactual number of nodes.

Example 21 is the non-transitory computer-readable storage medium ofExample 17, wherein the cluster load criterion reflects a number ofnodes that remain unassigned to any currently running application withina certain time period.

Example 22 is the non-transitory computer-readable storage medium ofExample 17, wherein the cluster load criterion reflects a number ofnodes that remain unassigned to any running application within a certaintime period, multiplied by the time period.

Example 23 is the non-transitory computer-readable storage medium ofExample 17, wherein determining the actual number of nodes furthercomprises: generating a plurality of combinations of applications to bescheduled; determining a value of the cluster load criterion for eachgenerated combination; and selecting a combination that is associatedwith an optimal value of the cluster load criterion.

Example 24 is a high-performance computing system, comprising: a controlnode; and a compute cluster comprising a plurality of compute nodes;wherein the control node is configured to: receive a compute nodeallocation request specifying an expected running time of an applicationand a requested number of compute nodes of the compute cluster;determine, in view of the compute node allocation request and a currentload on the plurality of compute nodes, an actual number of computenodes to be allocated to the application, wherein the actual number ofcompute nodes to be allocated to the application optimizes a clusterload criterion; and notify the application of the actual number ofcompute nodes.

Example 25 is the high-performance computing system of Example 24,wherein the control node is further configured to: cause the applicationto be executed using the actual number of nodes.

Example 26 is the high-performance computing system of Example 24,wherein notifying the application of the actual number of nodescomprises: storing the actual number of nodes in a memory data structurethat is employed to store a configuration of nodes associated with theapplication; and storing an address of the memory data structure in thecontrol register.

Example 27 is the high-performance computing system of Example 24,wherein storing the address of the memory data structure in the controlregister comprises obtaining a lock of the control register.

Example 28 is the high-performance computing system of Example 24,wherein the cluster load criterion reflects a number of nodes thatremain unassigned to any running application within a certain timeperiod.

Example 29 is the high-performance computing system of Example 24,wherein the cluster load criterion reflects a number of nodes thatremain unassigned to any running application within a certain timeperiod, multiplied by the time period.

Example 30 is the high-performance computing system of Example 24,wherein determining the actual number of nodes further comprises:generating a plurality of combinations of applications to be scheduled;determining a value of the cluster load criterion for each generatedcombination; and selecting a combination that is associated with anoptimal value of the cluster load criterion.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers or the like. The blocks describedherein may be hardware, software, firmware or a combination thereof.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “defining,” “receiving,” “determining,” “issuing,”“linking,” “associating,” “obtaining,” “authenticating,” “prohibiting,”“executing,” “requesting,” “communicating,” “monitoring,” “calculating,”or the like, refer to the actions and processes of a computing system,or similar electronic computing device, that manipulates and transformsdata represented as physical (e.g., electronic) quantities within thecomputing system's registers and memories into other data similarlyrepresented as physical quantities within the computing system memoriesor registers or other such information storage, transmission or displaydevices.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance or illustration. Any aspect or design described hereinas “example’ or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or.” That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an implementation” or “oneimplementation” or “an implementation” or “one implementation”throughout is not intended to mean the same implementation orimplementation unless described as such. Also, the terms “first,”“second,” “third,” “fourth,” etc. as used herein are meant as labels todistinguish among different elements and may not necessarily have anordinal meaning according to their numerical designation.

What is claimed is:
 1. A processing system, comprising: a controlregister; and a processing core, communicatively coupled to the controlregister; wherein the processing core is to: receive a node allocationrequest specifying an expected running time of an application and arequested number of nodes of a cluster of nodes; determine, in view ofthe node allocation request and a current load on the plurality ofnodes, an actual number of nodes to be allocated to the application,wherein the actual number of nodes to be allocated to the applicationoptimizes a cluster load criterion; store the actual number of nodes ina memory data structure; responsive to successfully obtaining a lock ofthe control register, storing an address of the memory data structure inthe control register; release the lock upon expiration of a pre-definedtimeout since obtaining the lock; and responsive to failing to obtainthe lock of the control register before expiration of a pre-definedtimeout, terminate the application.
 2. The processing system of claim 1,wherein the processing core is further to: cause the application to beexecuted using the actual number of nodes.
 3. The processing system ofclaim 1, wherein the cluster load criterion reflects a number of nodesthat remain unassigned to any currently running application within acertain time period.
 4. The processing system of claim 1, wherein thecluster load criterion reflects a number of nodes that remain unassignedto any running application within a certain time period, multiplied bythe time period.
 5. The processing system of claim 1, whereindetermining the actual number of nodes further comprises: generating aplurality of combinations of applications to be scheduled; determining avalue of the cluster load criterion for each generated combination; andselecting a combination that is associated with an optimal value of thecluster load criterion.
 6. A non-transitory computer-readable storagemedium comprising executable instructions that, when executed by aprocessing system, cause the processing system to perform operationscomprising: receiving a node allocation request specifying an expectedrunning time of an application, requested number of nodes of a clusterof nodes, and a waiting preference parameter indicating whether anadditional waiting time is preferred over allocating a lesser number ofnodes than the requested number of nodes; determining, in view of thenode allocation request and a current load on the plurality of nodes, anactual number of nodes to be allocated to the application, wherein theactual number of nodes to be allocated to the application optimizes acluster load criterion; and notifying, using a control register of theprocessing system, the application of the actual number of nodes.
 7. Thenon-transitory computer-readable storage medium of claim 6, whereinnotifying the application of the actual number of nodes comprises:storing the actual number of nodes in a memory data structure that isemployed to store a configuration of nodes associated with theapplication; and storing an address of the memory data structure in thecontrol register.
 8. The non-transitory computer-readable storage mediumof claim 7, wherein storing the address of the memory data structure inthe control register comprises obtaining a lock of the control register.9. The non-transitory computer-readable storage medium of claim 6,further comprising executable instructions causing the processing systemto: cause the application to be executed using the actual number ofnodes.
 10. The non-transitory computer-readable storage medium of claim6, wherein the cluster load criterion reflects a number of nodes thatremain unassigned to any currently running application within a certaintime period.
 11. The non-transitory computer-readable storage medium ofclaim 6, wherein the cluster load criterion reflects a number of nodesthat remain unassigned to any running application within a certain timeperiod, multiplied by the time period.
 12. The non-transitorycomputer-readable storage medium of claim 6, wherein determining theactual number of nodes further comprises: generating a plurality ofcombinations of applications to be scheduled; determining a value of thecluster load criterion for each generated combination; and selecting acombination that is associated with an optimal value of the cluster loadcriterion.
 13. The non-transitory computer-readable storage medium ofclaim 6, wherein notifying the application of the actual number of nodescomprises: responsive to failing to obtain a lock of the controlregister before expiration of a pre-defined timeout, terminate theapplication.
 14. A high-performance computing system, comprising: acontrol node; and a compute cluster comprising a plurality of computenodes; wherein the control node is to: receive a compute node allocationrequest specifying an expected running time of an application and arequested number of compute nodes of the compute cluster; determine, inview of the compute node allocation request and a current load on theplurality of compute nodes, an actual number of compute nodes to beallocated to the application, wherein the actual number of compute nodesto be allocated to the application optimizes a cluster load criterion;store the actual number of nodes in a memory data structure; responsiveto successfully obtaining a lock of the control register, store anaddress of the memory data structure in the control register; releasethe lock upon expiration of a pre-defined timeout since obtaining thelock; and responsive to failing to obtain the lock of the controlregister before expiration of a pre-defined timeout, terminate theapplication.
 15. The high-performance computing system of claim 14,wherein the control node is further to: cause the application to beexecuted using the actual number of nodes.
 16. The high-performancecomputing system of claim 14, wherein the cluster load criterionreflects a number of nodes that remain unassigned to any runningapplication within a certain time period.
 17. The high-performancecomputing system of claim 14, wherein determining the actual number ofnodes further comprises: generating a plurality of combinations ofapplications to be scheduled; determining a value of the cluster loadcriterion for each generated combination; and selecting a combinationthat is associated with an optimal value of the cluster load criterion.